昇思MindSpore学习笔记6-03计算机视觉--ResNet50图像分类

摘要：

        记录MindSpore AI框架使用ResNet50神经网络模型，选择Bottleneck残差网络结构对CIFAR-10数据集进行分类的过程、步骤和方法。包括环境准备、下载数据集、数据集加载和预处理、构建模型、模型训练、模型测试等。

一、概念

1.图像分类

最基础的计算机视觉应用

有监督学习类别

给定一张图像(猫、狗、飞机、汽车等等)

        判断图像所属的类别

使用ResNet50网络

        对CIFAR-10数据集进行分类

2.ResNet网络

ResNet50网络

        2015年微软实验室提出

        ILSVRC2015图像分类竞赛第一名

        传统卷积神经网络

                一系列卷积层和池化层堆叠

                堆叠到一定深度时会出现退化问题

56层网络与20层网络训练误差和测试误差图

        CIFAR-10数据集

        56层网络比20层网络训练误差和测试误差更大

        随着网络加深，误差并没有减小

3.残差网络结构

Residual Network

        减轻退化问题

        实现搭建较深的网络结构（突破1000层）

ResNet网络在CIFAR-10数据集上的训练误差与测试误差图

        虚线         训练误差

        实线         测试误差

        网络层数越深，训练误差和测试误差越小

二、环境准备

%%capture captured_output
# 实验环境已经预装了mindspore==2.2.14，如需更换mindspore版本，可更改下面mindspore的版本号
!pip uninstall mindspore -y
!pip install -i https://pypi.mirrors.ustc.edu.cn/simple mindspore==2.2.14
# 查看当前 mindspore 版本
!pip show mindspore

输出：

Name: mindspore
Version: 2.2.14
Summary: MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
Home-page: https://www.mindspore.cn
Author: The MindSpore Authors
Author-email: contact@mindspore.cn
License: Apache 2.0
Location: /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages
Requires: asttokens, astunparse, numpy, packaging, pillow, protobuf, psutil, scipy
Required-by: 

三、数据集准备与加载

1.数据集

CIFAR-10数据集

        60000张32*32的彩色图像

                50000张训练图片

                10000张评估图片

        10个类别

        每类有6000张图

2.下载数据集

download接口

        下载

        解压

        仅支持解析二进制版本的CIFAR-10文件（CIFAR-10 binary version）

from download import download
​
url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/cifar-10-binary.tar.gz"
​
download(url, "./datasets-cifar10-bin", kind="tar.gz", replace=True)

输出：

Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/cifar-10-binary.tar.gz (162.2 MB)

file_sizes: 100%|█████████████████████████████| 170M/170M [00:01<00:00, 113MB/s]
Extracting tar.gz file...
Successfully downloaded / unzipped to ./datasets-cifar10-bin
'./datasets-cifar10-bin'

数据集目录结构：

datasets-cifar10-bin/cifar-10-batches-bin
├── batches.meta.text
├── data_batch_1.bin
├── data_batch_2.bin
├── data_batch_3.bin
├── data_batch_4.bin
├── data_batch_5.bin
├── readme.html
└── test_batch.bin

3.加载数据集

mindspore.dataset.Cifar10Dataset接口

        加载数据集

        图像增强操作

import mindspore as ms
import mindspore.dataset as ds
import mindspore.dataset.vision as vision
import mindspore.dataset.transforms as transforms
from mindspore import dtype as mstype
​
data_dir = "./datasets-cifar10-bin/cifar-10-batches-bin"  # 数据集根目录
batch_size = 256  # 批量大小
image_size = 32  # 训练图像空间大小
workers = 4  # 并行线程个数
num_classes = 10  # 分类数量
​
def create_dataset_cifar10(dataset_dir, usage, resize, batch_size, workers):
​
    data_set = ds.Cifar10Dataset(dataset_dir=dataset_dir,
                                 usage=usage,
                                 num_parallel_workers=workers,
                                 shuffle=True)
​
    trans = []
    if usage == "train":
        trans += [
            vision.RandomCrop((32, 32), (4, 4, 4, 4)),
            vision.RandomHorizontalFlip(prob=0.5)
        ]
​
    trans += [
        vision.Resize(resize),
        vision.Rescale(1.0 / 255.0, 0.0),
        vision.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
        vision.HWC2CHW()
    ]
​
    target_trans = transforms.TypeCast(mstype.int32)
​
    # 数据映射操作
    data_set = data_set.map(operations=trans,
                            input_columns='image',
                            num_parallel_workers=workers)
​
    data_set = data_set.map(operations=target_trans,
                            input_columns='label',
                            num_parallel_workers=workers)
​
    # 批量操作
    data_set = data_set.batch(batch_size)
​
    return data_set
​
​
# 获取处理后的训练与测试数据集
​
dataset_train = create_dataset_cifar10(dataset_dir=data_dir,
                                       usage="train",
                                       resize=image_size,
                                       batch_size=batch_size,
                                       workers=workers)
step_size_train = dataset_train.get_dataset_size()
​
dataset_val = create_dataset_cifar10(dataset_dir=data_dir,
                                     usage=&